A100 SXM4 80GB on Denvr
Visit DenvrDenvr's NVIDIA A100 SXM4 80GB offering combines the enterprise-grade Ampere GPU—boasting 80GB HBM2e VRAM, 19.5 TFLOPS FP64, and up to 624 TFLOPS FP16 performance—with cutting-edge 100% liquid immersion cooling infrastructure. This enables high-density compute clusters with industry-leading Power Usage Effectiveness (PUE), making it noteworthy for sustainable AI and HPC workloads. Targeted at enterprises requiring Canadian data residency, scalable high-performance compute, and energy efficiency, it supports demanding ML training, inference, and data analytics. Key value propositions include modular data centers for rapid scaling, per-hour billing for cost predictability, and optimized thermal management that unlocks the A100's full potential without throttling. ML engineers gain reliable, green infrastructure ideal for large-scale models like LLMs, where density and efficiency directly impact TCO and environmental goals. While specific benchmarks are emerging, the combo promises superior uptime and density over air-cooled alternatives.
Why NVIDIA A100 SXM4 80GB on Denvr?
Opt for Denvr with NVIDIA A100 SXM4 80GB for its synergy of liquid immersion cooling and the GPU's 400W TDP, enabling ultra-high-density racks (up to 2x GPUs per space vs. air cooling) without thermal limits, boosting efficiency and reducing costs. This complements the A100's MIG multi-instance capabilities and NVLink interconnects for seamless multi-GPU scaling in AI workloads. Per-hour billing offers flexibility for bursty ML jobs, while Canadian data centers ensure residency compliance. Unique advantages include top-tier PUE (<1.1 estimated), modular deployments for enterprises, and sustainability focus—ideal when air-cooled providers hit density walls. Beats standard offerings in eco-efficiency and scale for production AI.
Live Pricing
Real-time NVIDIA A100 SXM4 80GB offers from Denvr
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
![]() Denvr | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 128 vCPU 1024GB RAM 15200GB Storage | Virginia | $1.15/GPU/hr $9.20/hr total (8×) | |||
![]() Denvr | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 208 vCPU 1024GB RAM 22800GB Storage | Virginia | $1.35/GPU/hr $10.80/hr total (8×) |


Performance Notes
Expect flagship A100 SXM4 80GB specs on Denvr: 19.5 TFLOPS FP64, 312 TFLOPS TF32, 2,496 TFLOPS sparse FP16, with 80GB HBM2e at 2 TB/s bandwidth. Liquid cooling sustains peak loads in dense clusters, minimizing throttling during extended training. Multi-GPU scaling excels via NVLink (600 GB/s) or likely InfiniBand (network bandwidth unspecified, assume 200-400 Gbps). Storage options unknown but expect fast NVMe SSDs for datasets. No public Denvr-specific MLPerf scores yet; performance mirrors reference A100s in similar immersion setups, with potential 10-20% efficiency gains from cooling. Monitor provider docs for interconnect details and benchmarks.
A provider focused on high-efficiency infrastructure using 100% liquid immersion cooling for energy-efficient, high-density compute clusters.
Best For
Unique Features
- Modular, liquid-immersion cooled data centers
- Industry-leading Power Usage Effectiveness (PUE)
VRAM
80GB
Architecture
Ampere
Tier
enterprise
Platform Features
Getting Started
Launch Denvr's NVIDIA A100 SXM4 80GB via their intuitive dashboard. Sign up, select pre-configured instances with NVIDIA drivers and CUDA, and deploy on-demand with per-hour billing. Supports Docker, NGC containers, and Jupyter for rapid ML prototyping to production-scale training. Canadian residency built-in for compliant workloads.
Steps
- 1Sign up for Denvr account and complete identity verification for access.
- 2Browse GPU catalog, select A100 SXM4 80GB node (e.g., 4-8 GPUs).
- 3Configure vCPU, RAM, NVMe storage, and networking options.
- 4Choose OS/image (Ubuntu/CUDA), review per-hour costs, and launch instance.
- 5Connect via SSH/Jupyter; install frameworks like PyTorch/TensorFlow.
Pro Tips
- Partition VRAM with MIG for concurrent jobs, maximizing 80GB utilization in multi-user environments.
- Exploit immersion cooling for 24/7 training runs; monitor dashboard for PUE and thermal metrics.
- Scale horizontally across clusters using NVLink; test NVLink topology for optimal multi-GPU perf.
Frequently Asked Questions
What is Denvr's billing model for NVIDIA A100 SXM4 80GB?▾
Denvr bills per-hour for GPU instances including NVIDIA A100 SXM4 80GB. Hourly billing means you pay for full hours even if your job completes mid-hour. Plan your workloads accordingly to maximize cost efficiency.
Does Denvr offer spot instances for NVIDIA A100 SXM4 80GB?▾
No, Denvr does not currently offer spot instances for NVIDIA A100 SXM4 80GB. All instances are billed at on-demand rates. However, they do offer reserved instances for committed usage, which can provide significant discounts for long-term workloads.
How can I access NVIDIA A100 SXM4 80GB instances on Denvr?▾
Denvr provides access to NVIDIA A100 SXM4 80GB instances via SSH, built-in Jupyter notebooks. The built-in Jupyter notebook support makes it easy to start experimenting immediately without additional setup. SSH access gives you full control over the instance for custom configurations and production deployments.
What compliance certifications does Denvr have for NVIDIA A100 SXM4 80GB workloads?▾
Denvr maintains GDPR certification, making it suitable for regulated workloads. Contact Denvr directly for detailed compliance documentation and BAA agreements if needed.
Can I use NVIDIA A100 SXM4 80GB with Kubernetes on Denvr?▾
Yes, Denvr supports Kubernetes for orchestrating NVIDIA A100 SXM4 80GB workloads. This enables you to deploy scalable ML pipelines, manage distributed training jobs across multiple GPUs, and integrate with MLOps tools like Kubeflow, Argo Workflows, and KServe. Kubernetes support is essential for teams building production-grade ML infrastructure.
What are the specifications of the NVIDIA A100 SXM4 80GB?▾
The NVIDIA A100 SXM4 80GB features 80GB of high-bandwidth memory, built on NVIDIA's Ampere architecture. As an enterprise-tier GPU, it's designed for large-scale AI training, inference at scale, and demanding HPC workloads. The substantial VRAM capacity supports large language models, complex neural networks, and multi-model deployments.
What workloads is NVIDIA A100 SXM4 80GB on Denvr best suited for?▾
The NVIDIA A100 SXM4 80GB on Denvr is well-suited for large-scale AI/ML training, LLM fine-tuning, batch inference at scale, and high-performance computing workloads. Denvr specifically excels at: Enterprises needing sustainable, high-density compute; Canadian data residency requirements. Consider your model size, training data volume, and latency requirements when evaluating this combination for your specific use case.
Does Denvr offer reserved instances for NVIDIA A100 SXM4 80GB?▾
Yes, Denvr offers reserved instance pricing for NVIDIA A100 SXM4 80GB, which can provide significant discounts (typically 20-40% off on-demand rates) for committed usage periods. Reserved instances are ideal for predictable, long-running workloads like production inference services, ongoing training pipelines, or development environments that run continuously. Contact Denvr for current reserved pricing and commitment terms.
What unique features does Denvr offer for NVIDIA A100 SXM4 80GB?▾
Denvr differentiates itself with: Modular, liquid-immersion cooled data centers; Industry-leading Power Usage Effectiveness (PUE). These features may provide advantages depending on your specific workflow requirements and technical needs. Evaluate how these capabilities align with your ML infrastructure goals when making your decision.
How do I get started with NVIDIA A100 SXM4 80GB on Denvr?▾
To get started with NVIDIA A100 SXM4 80GB on Denvr, visit https://www.denvrdata.com?utm_source=gpuperhour&utm_medium=referral to create an account. Most providers offer a straightforward signup process, and some provide initial credits for new users. Once registered, you can typically launch a NVIDIA A100 SXM4 80GB instance within minutes through their dashboard or API. We recommend starting with a small experiment to familiarize yourself with the platform before scaling up to larger workloads.
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